We will report on how media representations shape public perceptions of AI and then use what we learn to explore how we might better represent everyday interactions with AI to the public. We will begin by developing lists, taxonomies, and case studies of popular representations of AI and hypotheses about how the general public and elite groups discriminate between “good” and “bad” AI, and about how media representations help shape these perceptions. We will then test these hypotheses on members of the public and on groups of experts. The project will culminate in a festival featuring multiple media representations, including games, videos, and written works, all of which will draw on the insights gleaned from our focus group and survey research. Our research questions include:
- What drives popular negativity about AI? Does the public have its reasons that the experts know not of? Or have the public adopted views of AI borne of misrepresentations?
- Do overtly dystopian representations of AI feed, or perhaps temper, public outrage about insidious issues with AI and machine learning such as the biases of search algorithms?
- Can we produce narratives on AI in different media modalities that are more nuanced and complex than just the false dichotomy of good and bad? Could a more accurately critical (and yet still exciting) model of AI themed entertainment be developed, once we’ve gained an understanding of how the public has been encountering AI?
Sam Baker (English), Suzanne Scott (Radio-Television-Film), Paul Toprac (Computer Science)